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Proceedings Paper

Multisensor fusion using the sensor algorithm research expert system
Author(s): Michael E. Bullock; Thomas W. Miltonberger; Paul A. Reinholdsten; Kathleen Wilson
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Paper Abstract

A method for object recognition using a multisensor model-based approach has been developed. The sensor algorithm research expert system (SARES) is a sun-based workstation for model-based object recognition algorithm development. SARES is a means to perform research into multiple levels of geometric and scattering models, image and signal feature extraction, hypothesis management, and matching strategies. SARES multisensor fusion allows for multiple geometric representations and decompositions, and sensor location transformations, as well as feature prediction, matching, and evidence accrual. It is shown that the fusion algorithm can exploit the synergistic information contained in IR and synthetic aperture radar (SAR) imagery yielding increased object recognition accuracy and confidence over single sensor exploitation alone. The fusion algorithm has the added benefit of reducing the number of computations by virtue of simplified object model combinatorics. That is, the additional sensor information eliminates a large number of the incorrect object hypotheses early in the algorithm. This provides a focus of attention to those object hypotheses which are closest to the correct hypothesis.

Paper Details

Date Published: 1 August 1991
PDF: 12 pages
Proc. SPIE 1471, Automatic Object Recognition, (1 August 1991); doi: 10.1117/12.44887
Show Author Affiliations
Michael E. Bullock, Advanced Decision Systems (United States)
Thomas W. Miltonberger, Advanced Decision Systems (United States)
Paul A. Reinholdsten, Advanced Decision Systems (United States)
Kathleen Wilson, Advanced Decision Systems (United States)


Published in SPIE Proceedings Vol. 1471:
Automatic Object Recognition
Firooz A. Sadjadi, Editor(s)

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